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Record W2134353461 · doi:10.1109/iembs.1990.691254

Validation Of An Electrocardiographic Inverse Solution Using Percutaneous Transluminal Coronary Angioplasty

2005· article· en· W2134353461 on OpenAlexaff
Rob MacLeod, Martin J. Gardner, R. Glen Macdonald, Mark Henderson, R. Matthew Miller, B. Milan Horáček

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPercutaneous transluminal coronary angioplastyTorsoCardiologyElectrocardiographyInternal medicineInverseMedicineInverse problemBiplaneMagnetocardiographyAngioplastyHomogeneousIschemiaRadiologyMathematicsMathematical analysisMaterials scienceGeometryAnatomy

Abstract

fetched live from OpenAlex

Using a realistic homogeneous torso model, we have developed a solution to the inverse problem of electrocardiography in terms of epicardial potential distributions. This inverse solution has been validated directly using simulated potentials from a dipole source, and also indirectly by using body surface potential maps (BSPM) from patients undergoing percutaneous transluminal coronary angioplasty (PTCA). Isointegral difference maps were constructed by subtracting preinflation maps from those recorded at peak inflation; application of the inverse solution produced epicardial difference maps from which it was possible to infer regions of PTCA-induced ischemia. The predicted location of ischemia corresponded well with those expected from knowledge of the patient’s coronary circulation and the location of the PTCA balloon.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.264
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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